Importing data

load("./data/Wave 1/DS0001/34315-0001-Data.rda")
load("./data/Wave 2/DS0001/37105-0001-Data.rda")
load("./data/Wave 3/DS0001/37106-0001-Data.rda")

wave_1_data = da34315.0001
wave_2_data = da37105.0001
wave_3_data = da37106.0001

Creating Graphs

countplot = wave_3_data %>% 
  select(PH001, PH002, SEX) %>% 
  mutate(physical = case_when(
    PH001 == "(01) Excellent" ~ "Excellent", 
    PH001 == "(02) Very Good" ~ "Very Good",
    PH001 == "(03) Good" ~ "Good", 
    PH001 == "(04) Fair" ~ "Fair", 
    PH001 == "(05) Poor" ~ "Poor"), 
    
  mental = case_when(
    PH002 == "(01) Excellent" ~ "Excellent", 
    PH002 == "(02) Very Good" ~ "Very Good",
    PH002 == "(03) Good" ~ "Good", 
    PH002 == "(04) Fair" ~ "Fair", 
    PH002 == "(05) Poor" ~ "Poor"),
  
  sex = case_when(
    SEX == "(1) Male" ~ "Male",
    SEX == "(2) Female" ~ "Female"
  ),
  
  physical = factor(physical, levels = c("Excellent", "Very Good", "Good", "Fair", "Poor")), 
  
  mental = factor(mental, levels = c("Excellent", "Very Good", "Good", "Fair", "Poor"))
        ) %>% 
  select(-PH001, -PH002, -SEX)

plot_1 = 
  countplot %>%
  ggplot(aes(x = physical, y = mental, color = ..n..)) +
  geom_count(alpha = 0.8) +
  labs(
    x = "Self-Rated Physical Health",
    y = "Self-Rated Mental Health"
  ) +
  facet_grid(~sex) +
  theme_bw() +
  theme(legend.position = "none")

ggplotly(plot_1)
wave_1_data = wave_1_data %>% 
  mutate(MHUCLA_LONELINESS_1 = MHUCLA_LONELINESS)

wave_2_data = wave_2_data %>% 
  mutate(MHUCLA_LONELINESS_2 = MHUCLA_LONELINESS)

wave_3_data = wave_3_data %>% 
  mutate(MHUCLA_LONELINESS_3 = MHUCLA_LONELINESS)

spaghetti = 
  merge(wave_1_data, wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 

  select(ID, SEX.x, CS006, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  arrange(ID) %>% 
  mutate(sex = case_when(
    SEX.x == "(1) Male" ~ "Male",
    SEX.x == "(2) Female" ~ "Female"
  ),
    marital_status = case_when(
      CS006 == "(1) Married" ~ "Married",
      CS006 == "(2) Living with a partner as if married" ~ "Cohabitation",
      CS006 == "(3) Single(never married)" ~ "Single",
      CS006 == "(4) Separated" ~ "Separated",
      CS006 == "(5) Divorced" ~ "Divorced",
      CS006 == "(6) Widowed" ~ "Widowed"
)) %>% 
  filter(marital_status == "Widowed") %>% 
  select(-SEX.x, -CS006) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
      wave == "MHUCLA_LONELINESS_1" ~ 1, 
      wave == "MHUCLA_LONELINESS_2" ~ 2, 
      wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  janitor::clean_names()

spaghetti %>% 
  filter(sex == "Male", loneliness_value != "NA") %>% 
  ggplot(aes(x = wave, y = loneliness_value, color = id)) +
  geom_line() +
  theme_bw() +
  theme(legend.position = "none")

# ICD-10: bar graph of proportions of each ICD-10 code to determine which ICD-10 diagnosis accounts for the greatest burden of disease in our population. We can potentially facet by sex to see if the burden is different for males and females. 
bar_graph = 
  wave_3_data %>% 
  select(ID, ICD10_01:ICD10_16) %>% 
  summarize(total_01 = sum(ICD10_01), total_02 = sum(ICD10_02), total_03 = sum(ICD10_03), 
            total_04 = sum(ICD10_04), total_05 = sum(ICD10_05), total_06 = sum(ICD10_06), 
            total_07 = sum(ICD10_07), total_08 = sum(ICD10_08), total_09 = sum(ICD10_09),
            total_10 = sum(ICD10_10), total_11 = sum(ICD10_11), total_12 = sum(ICD10_12),
            total_13 = sum(ICD10_13), total_14 = sum(ICD10_14), total_15 = sum(ICD10_15),
            total_16 = sum(ICD10_16)) %>% 
  gather(key = icd_code, value = total, total_01:total_16) %>% 
  separate(icd_code, into = c("total_char","icd_code"), sep = "_") %>% 
  select(-total_char) %>% 
  mutate(burden = total/sum(total)*100) %>% 
  mutate(disease = case_when(
    icd_code == "01" ~ "Infectious diseases",
    icd_code == "02" ~ "Neoplasms",
    icd_code == "03" ~ "Blood diseases",
    icd_code == "04" ~ "Nutritional/ metabolic diseases",
    icd_code == "05" ~ "Mental/behavioral disorders",
    icd_code == "06" ~ "Nervous system diseases",
    icd_code == "07" ~ "Eye diseases",
    icd_code == "08" ~ "Ear diseases",
    icd_code == "09" ~ "Circulatory system diseases",
    icd_code == "10" ~ "Respiratory system diseases",
    icd_code == "11" ~ "Digestive system diseases",
    icd_code == "12" ~ "Skin diseases",
    icd_code == "13" ~ "Muscloskeletal system diseases",
    icd_code == "14" ~ "Genitourinary system diseases",
    icd_code == "15" ~ "Perinatal conditions",
    icd_code == "16" ~ "Congenital malformations"
  ))
 

bar_graph %>% 
  ggplot(aes(x = reorder(disease, -burden), y = burden)) +
  geom_bar(stat = "identity") + 
  labs(
    title = "Proportion of people with specified diseases",
    x = "Disease",
    y = "Proportion (%)"
  ) + 
  viridis::scale_color_viridis(
    name = "Location", 
    discrete = TRUE
  ) + 
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, size = 7))

### Baseline 0

baseline_loneliness_0 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 0 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_0 = baseline_loneliness_0 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) + 
    ylim(0,10)

### Baseline 1

baseline_loneliness_1 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 1 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_1 = baseline_loneliness_1 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

### Baseline 2

baseline_loneliness_2 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 2 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_2 = baseline_loneliness_2 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

### Baseline 3


baseline_loneliness_3 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 3 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_3 = baseline_loneliness_3 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

### Baseline 4

baseline_loneliness_4 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 4 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_4 = baseline_loneliness_4 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)


### Baseline 5

baseline_loneliness_5 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 5 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_5 = baseline_loneliness_5 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

### Baseline 6

baseline_loneliness_6 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 6 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_6 = baseline_loneliness_6 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

### Baseline 7

baseline_loneliness_7 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 7 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_7 = baseline_loneliness_7 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

### Baseline 8

baseline_loneliness_8 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 8 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_8 = baseline_loneliness_8 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

### Baseline 9

baseline_loneliness_9 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 9 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_9 = baseline_loneliness_9 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

### Baseline 10

baseline_loneliness_10 = wave_1_data %>% 
  filter(MHUCLA_LONELINESS_1 == 10 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>% 
  select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>% 
  merge(wave_2_data, by = "ID") %>% 
  merge(wave_3_data, by = "ID") %>% 
  select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>% 
  gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>% 
  mutate(wave = case_when(
    wave == "MHUCLA_LONELINESS_1" ~ 1,
    wave == "MHUCLA_LONELINESS_2" ~ 2,
    wave == "MHUCLA_LONELINESS_3" ~ 3
  )) %>% 
  filter(loneliness_value != "NA")

baseline_10 = baseline_loneliness_10 %>% 
  ggplot(aes(x = wave, y = loneliness_value)) +
    geom_smooth(se = FALSE) +
    ylim(0,10)

(baseline_0 + baseline_1 + baseline_2 + baseline_3 + baseline_4 + baseline_5 + baseline_6 + baseline_7 + baseline_8 + baseline_9)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 5.2454e-17
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.0661e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.1055e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.9961e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.0184e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.3477e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.1404e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.2425e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.01
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401